Browsing by Author "Shindell, Drew"

Previously, we usually analyze climate responses to all the climate drivers combined. However, the climate responses to individual climate drivers are far from well-known, as it is nearly impossible to separate the climate responses to individual climate drivers from the pure observational records. In this dissertation, I analyzed the responses of effective radiative forcing (ERF), precipitation and clouds to five individual climate drivers by using the model output from the Precipitation and Driver Response Model Inter-comparison Project (PDRMIP, consisting of five core experiments: CO2x2, CH4x3, Solar+2%, BCx10, and SO4x5). Firstly, I compared the ERF values estimated by six different methods and demonstrated that the values estimated using fixed sea-surface temperature and linear regression methods are fairly consistent for most climate drivers. For each individual driver, multi-model mean ERF values vary by 10-50% with different methods, and this difference may reach 70-100% for BC. Then, I analyzed the dynamical responses of precipitation in Mediterranean to well-mixed greenhouse gases (WMGHGs) and aerosols and found that precipitation in Mediterranean is more sensitive to BC forcing. When scaled to historical forcing level, WMGHG contributed roughly two-thirds to the Mediterranean drying during the past century and BC aerosol contributed the remaining one-third by causing a northward shift of the jet streams and storm tracks. Lastly, I explored the responses of shortwave cloud radiative effect (SWCRE) to CO2 and the two aerosol species and found that CO2 causes positive SWCRE changes over most of the Northern Hemisphere during boreal summer, and BC causes similar positive responses over North America, Europe and East China but negative SWCRE over India and tropical Africa. When normalized by global ERF, the change of SWCRE from BC forcing is roughly 3-5 times larger than that from CO2. SWCRE change is mainly due to cloud cover changes resulting from the changes in relative humidity, and to a lesser extent, changes in circulation and stability. The SWCRE response to sulfate aerosols, however, is negligible compared to that from CO2 and BC, because the radiation scattered by clouds under all-sky conditions will also be scattered by aerosols under clear-sky conditions. As SW is in effect only during daytime, positive (negative) SWCRE could amplify (dampen) daily maximum temperature (Tmax). Using a multi-linear regression model, I found that Tmax increases by 0.15 K and 0.13 K given unit increase in local SWCRE under the CO2 and BC experiments, respectively. When domain-averaged, SWCRE changes contributed to summer mean Tmax changes by 10-30% under CO2 forcing and by 30-50% under BC forcing, varying by regions, which can have important implications extreme climatic events and socio-economic activities.

Long-term exposure to ambient ozone (O3) is associated with a variety of impacts, including adverse health effects in humans (U.S. EPA 2013) and reduced yields in commercial crops (Chameides et al., 1994; Mauzerall and Wang, 2001). Due to such impacts, efforts have been undertaken in recent decades to reduce ground-level O3 through public policy regulating the emission of anthropogenic precursor emissions, such as nitrogen oxides (NOx) and volatile organic compounds (VOCs). These efforts have been widely successful in reducing peak concentrations (Simon et al., 2015; Lefohn et al., 2017; Fleming et al., 2018), but impacts related to both human-health and crop yields nonetheless persist (Cohen et al., 2017; Seltzer et al., 2018; Zhang et al., 2018; Shindell et al., 2019). Of particular importance, the maginute and trend of impacts reported in the literature often feature substantial differences (Seltzer et al., 2018; Zhang et al., 2018; Stanaway et al., 2018). As climate change is anticipated to exacerbate O3 pollution (Leibensperger et al., 2008; Jacob and Winner 2009; Nolte et al., 2018) and the emission of O3 precursors are projected to vary dramatically in both direction and region over the coming decades (Rao et al., 2017), there is a growing need to better constrain the magnitude and trends of, as well as illuminate the reason for the persistent differences in, impacts attributable to long-term O3 exposure. Here, I used a variety of modeling methods to explore the strengths and weaknesses of standard methods that are frequently used to simulate impact metrics related to air quality, generate a measurement-based estimate of the magnitude of O3 exposure and subsequent impacts within several populous regions of the world, and use machine learning to predict the trends in O3 exposure and subsequent impacts within the United States over an extended period.

First, I use the NASA GISS ModelE2 and GEOS-Chem models, each setup in a number of configurations, to simulate the near-present chemistry of the atmosphere and predict a number of impact metrics. Results featured minor differences due to the model resolution, whereas model, meteorology, and emissions inventory each drove large variances. Surface metrics related to O3 were consistently high biased and capturing the change in O3 metrics over time proved difficult, demonstrating the need to evaluate particular modeling frameworks before O3 impacts are quantified. Oftentimes, the configuration that captured the change of a metric best over time differed from the configuration that captured the magnitude of the same metric best, illustrating the difficulty in skillfully simulating and evaluating predicted impacts.

Then, I use data solely from dense ground-based monitoring networks in the United States, Europe, and China for 2015 to estimate long-term O3 exposure and calculate premature respiratory mortality using exposure-response relationships derived from two separate analyses of the American Cancer Society Cancer Prevention Study-II (ACS CPS-II) cohort. Results show that estimated impacts were quite different when using the two cohort analyses, with the analysis using the older ACS CPS-II cohort yielding approximately 32%–50% lower health impacts. In addition, both sets of results are lower (∼20%–60%) on a region-by-region basis than analogous prior studies based solely on CTM predicted O3, due in large part to the fact that the latter tends to be high biased in estimating exposure. I also demonstrate how small biases in modeled results of long-term O3 exposure can amplify health impacts due to nonlinear exposure-response relationships.

Finally, I develop and apply artificial neural networks to empirically model long-term O3 exposure over the continental United States from 2000-2015, generating a 16-year measurement-based assessment of impacts on human-health and crop yields. I again find that the impacts are quite different when using the two ACS CPS-II cohort studies, but I notably also report that the results differ in their trends over the study period due to the seasons included in each averaging metric. When using the older averaging metric and concentration-response function, there was a ~18% decrease in normalized human-health impacts. In contrast, there was little change in the newer averaging metric between 2000-2015, which resulted in a ~5% increase in normalized human-health impacts. In both cases, an aging population structure played a substantial role in modulating these trends. All agriculture-weighted crop-loss metrics indicate yield improvements over this period, with reductions in the estimated national relative yield loss ranging from 1.7-1.9 % for maize, 5.1-7.1% for soybeans, and 2.7% for wheat. Overall, the results from this study illustrate how different conclusions regarding historical impacts can be made through the use of varying metrics.

Air pollution, human health, and the economy are a connected system. Substantial efforts have been made in understanding and modeling these connections, so that we can predict the health and economic impacts of changes in air pollution to inform policymaking. However, simplifications and knowledge gaps are still present, and understanding of how these influence the entire system remain limited. This dissertation studies some of these simplifications and knowledge gaps, develops new tools to quantify them, and discusses their impacts to the system. These tools and impacts are used to facilitate more comprehensive evaluation of the impacts of air pollution, especially those that could have been underestimated, and to examine their sensitivity to certain assumptions. In general, air pollution has greater impacts than previously estimated, and future projections include assumptions that seem to be optimistic in the light of historical trajectories. The concluding chapter discusses implications for the sensitivities and uncertainties of the whole system, and how the system of economy-pollution-health should be integrated with the economy-climate system.

The relationships between economy and emissions of major air pollutants and greenhouse gases are basic inputs used to generate historical estimates and future emission scenarios. I show that these relationships vary over time across different widely-used global inventories, indicating the presence of large uncertainties within historical emission trajectories. More specifically, my examination of four major sectors (power, industry, residential, and transportation) and three pollutants (sulfur dioxide, carbon dioxide, and black carbon) demonstrates that long-term income-emission trajectories are both sector and pollutant specific. When assessing future projections of income-emission trajectories in reference scenarios, however, I show the persistence of faster rates of emission declines and estimates of earlier turnover incomes than estimated from historical data. This indicates some underlying uncertainties in such trajectories and that future projections of income-emission trajectories for integrated assessment should be used with appropriate caution.

In the next part of my analysis, I focus on the relationship between air pollution and health outcomes, which is based on epidemiological evidence. To date, epidemiologically-based quantitative relationships have been developed for many cardiovascular and respiratory diseases, as well as diabetes. A knowledge gap persists with respect to the exposure-response relationship between air pollution and incidence of dementia, due to relatively limited evidence. I assessed this exposure-response relationship by using a meta-analysis approach to collect data from existing epidemiological studies. I have developed an exploratory model of this relationship, and estimated that, globally, 1.1M [0.6M, 1.6M; 5-95% confidence] global incident cases and 0.34M [0.17M, 0.48M] premature deaths from dementia were attributable to ambient fine particulate matter (PM2.5) pollution in 2015. In addition, using model reconstructions of surface PM2.5 levels, I have shown that this burden of disease has grown 60~70% since 2000 as a consequence of globally increased exposures to ambient PM2.5. For the first time, our meta-analysis approach enables us to estimate that ambient PM2.5 pollution may be responsible for 15% of the premature deaths and 16% of the morbidity burdens associated with dementia across all risk factors.

The third linkage closes the loop of the air pollution, health, and the economy system, by estimating the direct economic costs associated with morbidity burdens (hospital admissions, emergency room visits, restricted activity days, etc.). Unlike mortality burdens, costs associated with morbidity burdens directly affect market activities. These costs include medical expenditures shared by households and the public health sector, productivity loss due to lost work hours, and the costs of private and public care for the ill. Identifying and estimating these costs is important for decision-making. For this analysis, I developed statistical models of seven exposure-response relationships for five acute morbidity endpoints, using data collected from meta-analysis. I quantified uncertainties associated with these exposure-response functions by randomization and simulation. I showed that these morbidity effects per unit increase in exposure generally reduce as exposures increase, unlike previous approaches that assume they are fixed over the entire range of exposures. Therefore, these functions are particularly useful to analyze impacts of air pollution in high-exposure regions or on a global scale.

I concluded that this dissertation demonstrated the importance of further understanding of uncertainties in this economy-pollution-health system. In particular, we should study interactions and propagations of uncertainty throughout this non-linear system. Moreover, research on the economy-pollution-health and the economy-climate systems should be more integrated, because the two systems overlap and the research methodology to analyze each also has many similarities. Insights from one system can inform challenges from the other system.